Algorithmic Shortlisting in Participatory Budgeting

[Submitted on 7 Aug 2025 (v1), last revised 16 Sep 2026 (this version, v4)]

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Abstract:Participatory budgeting is a democratic innovation that allows citizens to propose and vote on public investment projects. To help organizers manage large volumes of submissions, we design and test privacy-preserving methods for algorithmic shortlisting. These algorithms predict which projects are likely to be funded using only project features and anonymous historical voting data. We demonstrate the limitations of a naive approach that uses a large language model to rank projects based on past success and propose a vote-based pipeline that enables state-of-the-art LLMs to perform on par with classical machine learning. Our findings indicate that user preferences in participatory budgeting are stable enough to allow algorithmic shortlisting to approximate an initial selection of projects effectively.

Submission history

From: Juan Ignacio Zambrano [view email]
[v1] Thu, 7 Aug 2025 15:26:22 UTC (668 KB)
[v2] Tue, 3 Feb 2026 10:53:16 UTC (1,130 KB)
[v3] Mon, 6 Jul 2026 16:24:01 UTC (1,165 KB)
[v4] Wed, 16 Sep 2026 08:54:30 UTC (732 KB)